---

Statement Problem: The discourse model of participatory design is proposed in the world as a solution for the redesign of urban contexts. Extensive destructions in the historical contexts of Iran are due to the lack of implementation of the urban design discourse.
Aim: This research explores the pattern of discourse creation in participatory design with the approach of citizenship education in the historical context of Kazerun.
Methods: The qualitative research approach was coded by categorizing the clusters of the participating community and snowball sampling into 3 open, axial and selective categories, then it was interpreted with the content analysis technique.
Results: The categories extracted from the interviews were included in 7 axial factors and in 2 selective reasons, including residents' ignorance and incorrect urban management.
Conclusion: Considering the effective factors in the citizens' ignorance (lack of understanding of the characteristics of the context, social and economic factors), the urban designer as a facilitator, at the beginning with the aim of making the participants aware of the historical context and creating a context for central participation, provided citizenship education then with preliminary education Urban design helps to turn the proposal into the ideas of the participants. Then moderates the ideas and codifies them in the form of integrated and comprehensive policies and helps the city management to communicate the implementation policies of participatory urban design and solve the problems caused by incorrect policies in the field of urban management, economic, design and security.

---

The construction and maintenance of structural pavement was a high-cost problem in last decade. The mechanical properties of self compacting concrete (SCC) required important factors .From its mechanical properties, the compressive strength (CS) is necessary to investigate experimental and computational intelligence analysis in construction materials. Developing models with accurate estimation for this key property caused to saving costs and time and producing an optimal blend. Because of the many advantages, using of SCC in structures is increasing. Construction of precast-prefabricated components, with the use of concrete has also recently been considered. Concrete properties have significant role in precast-prefabricated girders behavior. Exact prediction of these properties is the base of member’s analysis and design. The main purpose of this study is presents new formulation to estimate the compressive strength of self-compacting concrete containing rice husk ash (RHA) using robust variant of genetic programming, namely gene expression programming (GEP) method. To evaluate the performance of the GEP-based proposed model, prediction was also done using classical data driven methods named artificial neural network (ANN) and multiple linear regression (MLR) models.  A large and reliable experimental database containing the results of 156 compressive strength of SCC incorporating RHA is collated through an extensive review of the literature. The performance of proposed models of CS is then assessed using the database, and the results of this evaluation are presented using selected performance measures. New expressions for the estimation of CS of SCC are developed based on the database. To evaluate the modeling performances of the proposed GEP models for CS, different statistical metrics were used. Correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) were used as the measure of precision. The results showed that the models developed using the aforementioned methods have accuracy over 90 percent in prediction of CS of SCC. The results of testing datasets are compared to experimental results and their comparisons demonstrate that the GEP model (R=0.94, RMSE= 4.308 and MAE=4.916) outperforms ANN (R=0.92, RMSE= 5.136 and MAE=5.624) and MLR (R=0.89, RMSE= 8.212 and MAE=9.472). Proposed models have a strong potential to predict compressive strength of self compacting concrete incorporating rice husk ash with great precision. The importance of different input parameters is also given for predicting the compressive strengths at various ages using gene expression programming. Performed sensitivity analysis to assign effective parameters on compressive strength indicates that cementitious binder content is the most effective variable in the mixture. The assessment results present that the performance of the proposed models are in close agreement with the experimental results. Moreover, the new GEP-based formulation provides improved estimates of the compressive strength of SCC compared to ANN and MLR models. The proposed design equation can readily be used for pre-design purposes or may be used as a fast check on deterministic solutions.
 

---

The utilization of concrete Incorporating with fibers is one of the proper issues of construction industry in last years. The main focus of this research to design a high performance self-compacted fiber reinforced concrete (SCFRC) by using an evolutionary algorithm, which is implemented in MATLAB. Crow Search Algorithm (CSA) and Genetic Algorithm (GA) are statistical ways which are developed by optimization based meta-heuristic solutions. A total of 67 concrete mixtures were considered by varying the levels of key factors affecting concrete strength of concrete, namely, water content (137.2-195 kg/m3), cement content (325.5-520 kg/m3), coarse aggregate content (722-920 kg/m3), fine aggregate content (804.9-960 kg/m3), nano silica content (0-49.6 kg/m3),percentage of volumetric of fibers (0-0.9 %), lime stone powder content (0-288.9 kg/m3) and superplasticizer content (1.75-10.5 kg/m3) were developed to design optimized mixture proportions. The objective function called maximizing concrete strength was formulated as an optimization problem on the basis of Multiple Linear Regression (MLR) method. The constrains including ratio of mixture proportions and absolute volume of mixture design were utilized to obtain an optimal-strength and cost-effective design. The concrete technological constraints were identified as the factors of experimental design for concrete production. The evolutionary implementation of results reached incorporating mixture proportions having strengths in range of 30 - 88.7 MPa. Five numerical examples for optimum mixture design of SCFRC were considered to evaluate the capability and efficiency of CSA and GA algorithm. These results were compared and concluded that CSA (3.38-14.49 % of mean error) performed better than GA (7.95-15.52 % of mean error) for this application. Also, the proposed evolutionary CSA and GA algorithms are found to be reliable and robustness tools to solve and optimize engineering and concrete technological problem.